基于卷积神经网络(CNN)的学习者情绪检测

R. A. Sukamto, Munir, S. Handoko
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引用次数: 2

摘要

本研究关注课堂学习者在学习过程中的情绪检测,认为情绪检测是提高学习过程有效性的重要手段。卷积神经网络(Convolutional Neural Network, CNN)是深度学习架构的一个分支,也是机器学习的一部分。实验通过人脸检测、图像改进和模型形成等几个阶段进行。使用660张图像作为训练数据,分类处理结果显示出良好的效果。使用4层CNN,即2个卷积层和2个子采样层,得到的准确率平均结果是很好的。在实验结果的基础上,系统还需要进一步开发,增加更具体的数据类和训练数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learners mood detection using Convolutional Neural Network (CNN)
This research concerns about classroom learners mood detection in learning process which is believed to be an important thing to increase learning process effectiveness. Convolutional Neural Network (CNN), a branch of deep learning architectures and a part of Machine Learning, was used as a method in this research. The experiments were conducted through several stages such as face detection, image improvement and model formation. There are 660 images used as training data and the classification process result showed a good result. The accuracy average result was considered as a good result by using 4 layers of CNN i.e. 2 convolutional layers and 2 subsampling layers. Based on the experiments result, the system needs to be developed further by adding more specific data class and training data.
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